Why fish hydroacoustics?

  • Non-invasive monitoring
  • Management: distinguish LT vs SMB
  • Question: can the FRC (45–170 kHz) classify species?
  • Outcome: scalable pipeline for species ID & trends

Animated sonar pulses bouncing off a fish

About the dataset

  • ~30k rows × 302 variables; two species (LT, SMB)
  • Processed in Echoview
  • Signals: F45–F170 (Frequency Response Curve)
  • Context: morphometrics, depth, speed, orientation
  • Focus today: frequency data only for classification

Acoustic fingerprints: the Frequency Response Curve (FRC)

  • Different echoes (45–170 kHz) → the FRC
  • Species show distinct curve shapes
  • Hypothesis: FRC alone can separate LT vs SMB
Figure 1

Which frequencies separate species?

  • Compare LT vs SMB at each frequency (standardised difference)
  • Peaks indicate highly discriminative frequencies

Figure 2

From curves to models

  • Each fish’s FRC (45–170 kHz) summarised
    • Quantiles (q20–q100) & Median
  • feasts features capture curve shape (ACF/PACF/STL)
  • H2O AutoML across GBM / DL / XGBoost
  • Grouped CV by fish, test on held-out set
pipeline raw 1) Raw Sonar F45–F170 per ping agg 2) Per-fish Aggregation Quantiles (q20–q100) / Median raw->agg per fish feats 3) feasts Features ACF / PACF / STL (shape) agg->feats shape descriptors note1 Grouped CV by fishNum (60/20/20) agg->note1 model 4) H2O AutoML GBM / Deep Learning / XGBoost feats->model features → classifier note2 Thresholds policy & OOF clamp [0.40–0.70] model->note2